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Regret Bounds for Prediction Problems
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- Geoff Gordon
We present a unified framework for reasoning about worst-case regret
bounds for learning algorithms. This framework is based on the theory
of duality of convex functions. It brings together results from
computational learning theory and Bayesian statistics, allowing us to
derive new proofs of known theorems, new theorems about known
algorithms, and new algorithms.